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An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

Praveen Talari1,*, A. Suresh2, M. G. Kavitha3

1 Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
2 AMET University, Chennai, 600062, India
3 University College of Engineering, Pattukkottai, 600062, India

* Corresponding Author: Praveen Talari. Email:

Intelligent Automation & Soft Computing 2023, 35(1), 1053-1067.


As per World Health Organization report which was released in the year of 2019, Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world. Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it. Among the diabetics, it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2. To avoid this situation, we propose a new fuzzy logic based neural classifier for early detection of diabetes. A set of new neuro-fuzzy rules is introduced with time constraints that are applied for the first level classification. These levels are further refined by using the Fuzzy Cognitive Maps (FCM) with time intervals for making the final decision over the classification process. The main objective of this proposed model is to detect the diabetes level based on the time. Also, the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction. The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.


Cite This Article

P. Talari, A. Suresh and M. G. Kavitha, "An intelligent medical expert system using temporal fuzzy rules and neural classifier," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 1053–1067, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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